B.Tech CSI-401�Topic :� Concepts of Big Data & Analytics�
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Contents
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IOT & BIGDATA
A large number of communication devices in the IoT paradigm are embedded into sensor devices in the real world.
Data collecting devices sense data and transmit these data using embedded communication devices.
The continuum of devices and objects are interconnected through a variety of communication solutions, such as Bluetooth, WiFi, ZigBee.
These communication transmit data and receive commands from remotely controlled devices, which allow direct integration with the physical world through computer-based systems to improve living standards.
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IOT
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Role of BigData in IOT
NEED:
The development of big data and the Internet of things (IoT) is rapidly accelerating and affecting all areas of technologies and businesses by increasing the benefits for organizations and individuals.
The growth of data produced via IoT has played a major role on the big data landscape.
Big data can be categorized to three aspects: (a) volume, (b) variety, and (c) velocity
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Immense opportunities are presented by the capability to analyze and utilize huge amounts of IoT data, including applications in smart cities, smart transport and grid systems, energy smart meters, and remote patient healthcare monitoring devices.
Through IOT devices data are to be collected, such data are not useful without analytic.
Numerous big data, IoT, and analytics solutions have enabled people to obtain valuable insight into large data generated by IoT devices.
Role of BigData in IOT
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Relationship between IoT and big data analytics
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Big Data
Big data is high volume , high velocity and high variety information.
• Huge volume of data : Not just thousands/millions, but billions of items
• Complexity of data types and structures : Varity of sources, formats, structures
• Speed of new data creation and grow: High velocity, rapid ingestion, fast analysis
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Big Data
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What is big data analytics?
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BigData analysis is the process of examining, transforming, and arranging raw data in a specific way to generate useful information from it.
Big data analytics is the use of advanced analytic techniques or the scientific process of transforming big data into insights for making better decisions.
Analytics, is the use of data, information technology, statistical analysis, quantitative methods, and mathematical or computer-based models to help managers gain improved insight about their business operations and make better, fact-based decisions – by James Evans
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What is Analytics?
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The colloquial definition of analytics is
if we combine massive amounts of data (Big Data) along with powerful analytics ( advanced analytics) there is a greater deal of texture or context to predict future outcomes,
which allows us to then take it to the next level of using those insights to automate actions or to provide a short list of actions that are likely to result in significantly improved outcomes.
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Big Data Analytics
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Types of Data Analytics
Analytics is of four types
They can describe in detail about an event that has occurred in the past.
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Types of Data Analytics
2.) Predictive Analytics :Predictive analytics refers to using historical data, machine learning, and artificial intelligence to predict what will happen in the future. This historical data is fed into a mathematical model that considers key trends and patterns in the data. The model is then applied to current data to predict what will happen next.
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Types of Data Analytics
3) Prescriptive Analytics : Set of techniques to indicate the best course of action. It tells what decision to make to optimize the outcome. So that quality , productivity can improve.
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Analytic Types/Level | Specified Use | Existing Architectures/Tools | Advantages/Category |
Real time | To analyze the large amounts of data generated by the sensors | Greenplum / HANA | Parallel processing �clusters using traditional �databases memory based �computing platforms |
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Comparisons Between Analytics
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Examples Of Big Data Analytics In Industries
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Healthcare
In Healthcare big data analytics drive quicker responses to emerging diseases and improve direct patient care, the customer experience, and administrative, insurance and payment processing.
Financial services
In Financial analytics improve customer targeting using customer analytics. Businesses can make better informed underwriting decisions and provide better claims management while mitigating risk and fraud.
Communications service providers (CSPs)
CSPs can use big data analytics to optimize network monitoring, management and performance to help mitigate risk and reduce costs. They can also use analytics to improve customer targeting and service.
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Conclusion
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References
• Getting started with Internet of Things, by Cuno Pfister, Shroff; First edition (17 May 2011), ISBN-10:9350234130
• Big Data and The Internet of Things, by Robert Stackowiak, Art licht, Springer Nature; 1st ed. Edition (12 May 2015), ISBN-10: 1484209877
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Thank you
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